I conduct a proof-of-concept simulation study to examine the finite-sample performance of 2SMAIC with respect to the standard MAIC when the index study is an RCT. In this setting, the treatment assignment mechanism (the true conditional probability of treatment among the subjects) is known and, in expectation, independent of the covariates. Nevertheless, I find that modeling this probability is beneficial to control for random finite-sample imbalances between study arms, which often arise due to chance. This improves the precision and efficiency of MAIC without introducing bias. The results are consistent with previous research on the efficiency of propensity score estimators [1]}, [2]}. Finally, the use of weight truncation in combination with MAIC is explored for the first time. Example code to implement the methodologies in R is provided in Additional file 1.
